This invention relates to the three-dimensional geological modeling and the characterization of subsurface reservoirs of interest.
In the on-going search for subsurface hydrocarbons, methods have been developed for evaluating and interpreting the structure and characteristics of the earth's subsurface. Of particular importance is the ascertainment of formation characteristics consistent with the presence of hydrocarbon deposits.
There is an increasing need for reservoir characterization within the oil and gas industry. The need for reservoir characterization is mostly driven by economic realities: if reservoirs can be defined better using available technology then the end result is higher drilling success and fewer development wells. As part of the characterization and development of an oil or gas field, it is often required that a computer model of the subsurface reservoir be built. As the exact characteristics of the earth's subsurface cannot be clearly defined, multiple computer models which are various examples of the possible facies and their associated properties are developed.
Developing accurate reservoir models is a key objective of companies in the oil and gas industry. A properly constrained reservoir model can be used to quantify hydrocarbons in place and to optimize hydrocarbon production. The evaluation of reservoirs is typically achieved using a combination of seismic and well data. Each of these data represents imperfect measurements with a certain level of error. The manner in which these errors are handled affects the integration of the two data types and determines the quality of the final reservoir model.
It is common practice to obtain data about a reservoir from well logging instruments moved through existing wells in the reservoir. Such well data obtained from the well through well logs of various types represent data samples from only a small fraction of a reservoir's volume. For effective evaluation of a reservoir, knowledge of the actual rock formation (lithology or lithofacies) and the contained fluids, as well as the relative presence or volume of pore space (or its porosity), is needed. The fact that a reservoir exhibits certain lithofacies and porosity at a well provide no assurances that other areas of the reservoir have the same characteristics. Geological models of lithofacies and porosity that are based solely on well data thus contain large regions that are not based on actual sampled data; rather, the data must be estimated from the closest existing wells.
Three-dimensional seismic surveys provide data samples over most of a reservoir's volume, including portions unsampled by wells, but at best the seismic data can provide only indirect measurements of lithofacies and porosity. Existing seismic surveying technology does not offer techniques to measure either of these formation characteristics directly.
Geological modeling of the subsurface has been performed for many years by geologists, geophysicists, engineers and hydrologists. Many descriptive or static 3-D geological models built for mining or petroleum applications have been in the form of a specified three-dimensional array of individual model units or blocks (also called cells). One particular prior art methodology used to build reservoir property models has been impedance inversion. In this prior art method, the seismic data is inverted directly using a sparseness constraint for acoustic impedance values. One of the stumbling blocks in that method is the need for a low frequency model to compensate for the fact that the seismic data is bandlimited and therefore does not contain low frequencies. The low frequency model is typically generated from well data, in conjunction with seismic interpretation. One issue is that the low frequency model can be inadequate where there is limited well data available. A further issue is that seismic data also does not contain high frequencies, and with the above-described approach, the sparse reflectivity model which attempts to compensate for the lack of high frequencies can be inconsistent with the actual geological setting for which the inversion is being performed. This leads to poor estimates of the missing high frequency components. In addition, since this process is typically performed on a trace by trace basis no attempt is made to conform to the actual spatial statistics that are consistent with the actual well data.
In an attempt to include spatial statistics in the impedance inversion approach, stochastic inversion methods have been developed. These methods incorporate spatial statistics in the form of spatial variograms derived from available well data. A starting model is generated by interpolating the real well data using the spatial variograms and any existing seismic interpretation. This model is then iteratively updated until a sufficient match between the observed seismic data and the updated model, which obeys the required spatial constraints, is reached. Major drawbacks in this approach are the problems associated with the sparse well situation where the spatial variogram is poorly determined and the difficulty of getting a good match to the seismic when the starting model is poorly defined.
Both of the above-described approaches neglect the advantage offered by utilizing prior knowledge of the possible stratigraphic layering of the sediments over the area of interest. One approach which addresses this issue has been developed by dGB Earth Sciences. That approach utilizes the concept of geologically designed pseudo or synthetic wells to capture the deterministic nature of depositional environments together with probabilistic distributions of lithologies and elastic properties. A paper by de Groot P., Bril A., Florist F. and Campbell A., Monte Carlo Simulation of Wells, Geophysics, Vol. 61, No. 3 (May-June 1996); pp. 631-638 describes a methodology where 1-D stratigraphic profiles of pseudo-wells with attached physical properties, but without spatial information, are simulated using a combination of geological knowledge and Monte Carlo statistics. The paper describes the advantages of the described-method as being able to steer the algorithm with rules based on geological reasoning, and that hard constraints for the stochastic variables can be included.
While the methodology described by de Groot and Bril is a step in the right direction, there is a need for an improved method which is more closely constrained by actual depositional geology, has the ability to use dynamic pseudo-wells and which expands the functionality of the method.